Papers by Yuqing Zhu
Navigating the Shortcut Maze: A Comprehensive Analysis of Shortcut Learning in Text Classification by Language Models (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Language models (LMs) often rely on spurious correlations rather than causally relevant features to improve accuracy and generalizability. |
| Approach: | They propose a benchmark that categorizes shortcuts into occurrence, style, and concept . they aim to explore the nuanced ways shortcuts influence the performance of LMs . |
| Outcome: | The proposed benchmark categorizes shortcuts into occurrence, style, and concept . it systematically investigates models’ resilience and susceptibilities to sophisticated shortcuts . |
AC-EVAL: Evaluating Ancient Chinese Language Understanding in Large Language Models (2024.findings-emnlp)
Copied to clipboard
| Challenge: | AC-EVAL is a benchmark designed to assess the advanced knowledge and reasoning capabilities of LLMs within the context of ancient Chinese. |
| Approach: | They propose a benchmark to assess the advanced knowledge and reasoning capabilities of LLMs in ancient Chinese. |
| Outcome: | AC-EVAL aims to assess the comprehension of ancient Chinese texts . the benchmark covers 13 tasks covering historical facts, geography, social customs, art, philosophy, classical poetry and prose. |
Multi-grained Attention with Object-level Grounding for Visual Question Answering (P19-1)
Copied to clipboard
| Challenge: | Current approaches to visual question answering train attention models from coarse-grained associations between sentences and images, which fail on small objects or uncommon concepts. |
| Approach: | They propose a multi-grained attention method that learns explicit word-object correspondence by word-level attention complementary to the sentence-image association. |
| Outcome: | The proposed method achieves competitive performance with state-of-the-art models on the VQA benchmark. |
When LLMs Read Tables Carelessly: Measuring and Reducing Data Referencing Errors (2026.acl-long)
Copied to clipboard
Yuqing Yang, Qi Zhu, Zhen Han, Boran Han, Zhengyuan Shen, Shuai Wang, Vassilis N. Ioannidis, Huzefa Rangwala
| Challenge: | Large language models (LLMs) perform well on table tasks, but they still make data referencing errors (DREs) prior studies have only offered limited, small-scale analyses. |
| Approach: | They propose inference-time strategies and lightweight critics to mitigate data referencing errors. |
| Outcome: | The proposed model achieves an average F1 score of 78.2% in detecting both in-distribution and out-of-difference DREs and assists inference for larger models. |
SportQA: A Benchmark for Sports Understanding in Large Language Models (2024.naacl-long)
Copied to clipboard
Haotian Xia, Zhengbang Yang, Yuqing Wang, Rhys Tracy, Yun Zhao, Dongdong Huang, Zezhi Chen, Yan Zhu, Yuan-fang Wang, Weining Shen
| Challenge: | SportQA is a benchmark specifically designed for evaluating Large Language Models (LLMs) sports knowledge is characterized by its fast pace, variety of types, abundance of strategies, and rich player narratives . |
| Approach: | They propose a benchmark specifically designed for evaluating Large Language Models in the context of sports understanding. |
| Outcome: | The proposed benchmark aims to bridge the gap between existing and specialized benchmarks in sports understanding. |
AMAS: Adaptively Determining Communication Topology for LLM-based Multi-agent System (2025.emnlp-industry)
Copied to clipboard
| Challenge: | Large language models (LLMs) have revolutionized natural language processing, but their practical implementation as autonomous multi-agent systems remains fraught with unresolved challenges. |
| Approach: | They propose a dynamic graph selector that redefines LLM-based MAS by exploiting the intrinsic properties of individual inputs to intelligently direct query trajectories. |
| Outcome: | The proposed framework exceeds state-of-the-art approaches in question answering, mathematical deduction, and code generation benchmarks. |
Fighting Spurious Correlations in Text Classification via a Causal Learning Perspective (2025.naacl-long)
Copied to clipboard
| Challenge: | In text classification tasks, models often rely on spurious correlations for predictions, incorrectly associating irrelevant features with the target labels. |
| Approach: | They propose a Causally Calibrated Robust Classifier which integrates a causal feature selection method based on counterfactual reasoning and an unbiased inverse propensity weighting (IPW) loss function. |
| Outcome: | The proposed method achieves state-of-the-art performance among methods without group labels and can compete with the models that utilize group labels. |
TokenShapley: Token Level Context Attribution with Shapley Value (2025.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) have strong capabilities in in-context learning, but verifying the correctness of their generated responses remains a challenge. |
| Approach: | They propose a token-level attribution method that combines Shapley value-based data attribution with KNN-based retrieval techniques to improve attribution accuracy. |
| Outcome: | TokenShapley outperforms state-of-the-art methods on four benchmarks . it achieves an 11–23% improvement in accuracy on the benchmarks. |